42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
"""
|
|
Z-Image-Turbo ComfyUI Nodes - Optimized & Verified
|
|
===================================================
|
|
Based on official Tongyi-MAI/Z-Image repository and HuggingFace diffusers implementation.
|
|
|
|
CRITICAL FACTS about Z-Image-Turbo:
|
|
1. Distilled using Decoupled-DMD on LINEAR sigma schedule (1.0 → 0.0)
|
|
2. Optimal: 8-9 steps, CFG=1.0, Euler sampler
|
|
3. Any deviation from linear schedule BREAKS the distillation
|
|
4. The model expects sigma values as-is - no "recalibration" needed
|
|
|
|
WHY CUSTOM SAMPLERS?
|
|
ComfyUI's built-in euler uses the Karras ODE formulation with s_churn machinery.
|
|
Our samplers use the clean interpolation formula which is:
|
|
- More numerically stable (no division by small sigma)
|
|
- Cleaner (no s_churn/s_tmin/s_tmax)
|
|
- Explicit handling of final step (sigma_next = 0)
|
|
|
|
Available samplers (in KSampler dropdown):
|
|
- euler_flow: Clean 1st order Euler
|
|
- euler_flow_alt: Derivative formulation
|
|
- euler_flow_verbose: With debug output
|
|
- midpoint_flow: 2nd order midpoint
|
|
- heun_flow: 2nd order Heun
|
|
|
|
Author: Capitan01R
|
|
Version: 1.0.0
|
|
"""
|
|
|
|
from .zimage_turbo_core import (
|
|
NODE_CLASS_MAPPINGS,
|
|
NODE_DISPLAY_NAME_MAPPINGS,
|
|
)
|
|
|
|
__all__ = [
|
|
"NODE_CLASS_MAPPINGS",
|
|
"NODE_DISPLAY_NAME_MAPPINGS",
|
|
]
|
|
|
|
__version__ = "1.0.0"
|
|
__author__ = "Capitan01R"
|